A typical forecasting system allows the user to explore the data, build forecasting models and analyze the forecasting accuracy. Forecasting accuracy is essential and needs to be monitored, such as continuously on a weekly basis. If the accuracy falls below a desirable level, the models should be rebuilt. For retail applications, datasets are massive, timely and efficient model building is critical, and reasonable forecast accuracy is essential to meet the business needs.
As illustrated in FIG. 1, forecasting accuracy and model (re)building 34 are traditionally done on a different copy of the database (e.g., data mart 32) than the production system's database (e.g., data mart 42). Typically, the analysis is performed in the user's sand box 30, wherein a sand box is a testing environment that separates or isolates untested code or model (re)building operations and changes from the production environment or repository. This separation is used since the production environment 40 and its programs 44 should remain stable and functional while exploration and monitoring continues in the sandbox environment 30. However, the maintenance of two separate data marts (32 and 42) can be costly in terms of resources, and it can present logistical problems whenever the data marts (32 and 42) must be updated, maintained, etc. These problems are particularly acute for retailers as their data marts are extremely large and the model rebuilding/exploration exercises require a significant investment of both time and space.